Overview

Dataset statistics

Number of variables16
Number of observations978949
Missing cells170252
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory119.5 MiB
Average record size in memory128.0 B

Variable types

Numeric12
Categorical4

Alerts

time has a high cardinality: 40240 distinct values High cardinality
gameId is highly correlated with teamHigh correlation
frameId is highly correlated with s and 1 other fieldsHigh correlation
s is highly correlated with disHigh correlation
dis is highly correlated with sHigh correlation
team is highly correlated with gameIdHigh correlation
nflId has 42563 (4.3%) missing values Missing
jerseyNumber has 42563 (4.3%) missing values Missing
o has 42563 (4.3%) missing values Missing
dir has 42563 (4.3%) missing values Missing
s has 60657 (6.2%) zeros Zeros
a has 56618 (5.8%) zeros Zeros
dis has 62790 (6.4%) zeros Zeros

Reproduction

Analysis started2022-11-02 14:58:25.170610
Analysis finished2022-11-02 14:59:54.147409
Duration1 minute and 28.98 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

gameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021103636
Minimum2021102800
Maximum2021110100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:54.192087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2021102800
5-th percentile2021102800
Q12021103103
median2021103107
Q32021103110
95-th percentile2021110100
Maximum2021110100
Range7300
Interquartile range (IQR)7

Descriptive statistics

Standard deviation1880.878366
Coefficient of variation (CV)9.306194561 × 10-7
Kurtosis7.884864592
Mean2021103636
Median Absolute Deviation (MAD)4
Skewness3.140901101
Sum1.978557383 × 1015
Variance3537703.428
MonotonicityIncreasing
2022-11-02T11:59:54.281824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
202110310682363
 
8.4%
202110311278752
 
8.0%
202111010076314
 
7.8%
202110310972197
 
7.4%
202110310872013
 
7.4%
202110311170771
 
7.2%
202110310769483
 
7.1%
202110310164285
 
6.6%
202110311061502
 
6.3%
202110310561088
 
6.2%
Other values (5)270181
27.6%
ValueCountFrequency (%)
202110280051060
5.2%
202110310050462
5.2%
202110310164285
6.6%
202110310258075
5.9%
202110310354901
5.6%
202110310455683
5.7%
202110310561088
6.2%
202110310682363
8.4%
202110310769483
7.1%
202110310872013
7.4%
ValueCountFrequency (%)
202111010076314
7.8%
202110311278752
8.0%
202110311170771
7.2%
202110311061502
6.3%
202110310972197
7.4%
202110310872013
7.4%
202110310769483
7.1%
202110310682363
8.4%
202110310561088
6.2%
202110310455683
5.7%

playId
Real number (ℝ≥0)

Distinct913
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2172.301201
Minimum54
Maximum4750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:54.396527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile230
Q11144
median2120
Q33259
95-th percentile4109
Maximum4750
Range4696
Interquartile range (IQR)2115

Descriptive statistics

Standard deviation1249.681091
Coefficient of variation (CV)0.5752798417
Kurtosis-1.151286371
Mean2172.301201
Median Absolute Deviation (MAD)1073
Skewness0.04065043933
Sum2126572088
Variance1561702.829
MonotonicityNot monotonic
2022-11-02T11:59:54.526683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29545152
 
0.5%
21204393
 
0.4%
11823335
 
0.3%
36173289
 
0.3%
622898
 
0.3%
39482829
 
0.3%
7652806
 
0.3%
1892806
 
0.3%
32792783
 
0.3%
19702691
 
0.3%
Other values (903)945967
96.6%
ValueCountFrequency (%)
54897
 
0.1%
552070
0.2%
622898
0.3%
751564
0.2%
76713
 
0.1%
791886
0.2%
84759
 
0.1%
86690
 
0.1%
97782
 
0.1%
991840
0.2%
ValueCountFrequency (%)
4750713
0.1%
4728690
0.1%
46701081
0.1%
4625759
0.1%
4603966
0.1%
45611058
0.1%
4518989
0.1%
45151242
0.1%
44891173
0.1%
4478851
0.1%

nflId
Real number (ℝ≥0)

MISSING

Distinct1087
Distinct (%)0.1%
Missing42563
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean45886.90232
Minimum25511
Maximum54038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:54.654981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25511
5-th percentile38538
Q142471
median45785
Q348455
95-th percentile53502
Maximum54038
Range28527
Interquartile range (IQR)5984

Descriptive statistics

Standard deviation5020.932834
Coefficient of variation (CV)0.1094197381
Kurtosis-0.06878397042
Mean45886.90232
Median Absolute Deviation (MAD)3242
Skewness-0.1922299703
Sum4.296785291 × 1010
Variance25209766.53
MonotonicityNot monotonic
2022-11-02T11:59:54.776281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
461062124
 
0.2%
433532124
 
0.2%
385692124
 
0.2%
432912124
 
0.2%
433072124
 
0.2%
399472124
 
0.2%
524422124
 
0.2%
479712124
 
0.2%
461102124
 
0.2%
460752124
 
0.2%
Other values (1077)915146
93.5%
(Missing)42563
 
4.3%
ValueCountFrequency (%)
255111440
0.1%
289631095
0.1%
295501517
0.2%
29851953
0.1%
30842422
 
< 0.1%
330841248
0.1%
331071181
0.1%
3324175
 
< 0.1%
335661161
0.1%
344521042
0.1%
ValueCountFrequency (%)
54038148
 
< 0.1%
53994100
 
< 0.1%
53960127
 
< 0.1%
53959243
 
< 0.1%
53954100
 
< 0.1%
53953695
0.1%
53946276
 
< 0.1%
5392182
 
< 0.1%
53910313
 
< 0.1%
53900923
0.1%

frameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.51631699
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:54.912835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q332
95-th percentile48
Maximum97
Range96
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.53702383
Coefficient of variation (CV)0.6456217434
Kurtosis0.5457487922
Mean22.51631699
Median Absolute Deviation (MAD)10
Skewness0.726804708
Sum22042326
Variance211.3250618
MonotonicityNot monotonic
2022-11-02T11:59:55.038948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123736
 
2.4%
1223736
 
2.4%
2123736
 
2.4%
2023736
 
2.4%
1923736
 
2.4%
1823736
 
2.4%
1723736
 
2.4%
1623736
 
2.4%
1523736
 
2.4%
1423736
 
2.4%
Other values (87)741589
75.8%
ValueCountFrequency (%)
123736
2.4%
223736
2.4%
323736
2.4%
423736
2.4%
523736
2.4%
623736
2.4%
723736
2.4%
823736
2.4%
923736
2.4%
1023736
2.4%
ValueCountFrequency (%)
9723
 
< 0.1%
9623
 
< 0.1%
9523
 
< 0.1%
9423
 
< 0.1%
9346
< 0.1%
9246
< 0.1%
9169
< 0.1%
9069
< 0.1%
8969
< 0.1%
8869
< 0.1%

time
Categorical

HIGH CARDINALITY

Distinct40240
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2021-10-31T19:31:54.400
 
69
2021-10-31T17:28:05.000
 
69
2021-10-31T17:28:04.100
 
69
2021-10-31T17:28:04.200
 
69
2021-10-31T17:28:04.300
 
69
Other values (40235)
978604 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters22515827
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-10-29T00:27:23.000
2nd row2021-10-29T00:27:23.100
3rd row2021-10-29T00:27:23.200
4th row2021-10-29T00:27:23.300
5th row2021-10-29T00:27:23.400

Common Values

ValueCountFrequency (%)
2021-10-31T19:31:54.40069
 
< 0.1%
2021-10-31T17:28:05.00069
 
< 0.1%
2021-10-31T17:28:04.10069
 
< 0.1%
2021-10-31T17:28:04.20069
 
< 0.1%
2021-10-31T17:28:04.30069
 
< 0.1%
2021-10-31T17:28:04.40069
 
< 0.1%
2021-10-31T17:28:04.50069
 
< 0.1%
2021-10-31T17:28:04.60069
 
< 0.1%
2021-10-31T17:28:04.70069
 
< 0.1%
2021-10-31T17:28:04.80069
 
< 0.1%
Other values (40230)978259
99.9%

Length

2022-11-02T11:59:55.148266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-10-31t19:31:54.40069
 
< 0.1%
2021-10-31t19:31:54.70069
 
< 0.1%
2021-10-31t18:58:16.70069
 
< 0.1%
2021-10-31t18:58:16.60069
 
< 0.1%
2021-10-31t18:58:16.50069
 
< 0.1%
2021-10-31t18:58:16.40069
 
< 0.1%
2021-10-31t18:58:16.30069
 
< 0.1%
2021-10-31t19:28:46.80069
 
< 0.1%
2021-10-31t18:05:12.00069
 
< 0.1%
2021-10-31t18:58:16.10069
 
< 0.1%
Other values (40230)978259
99.9%

Most occurring characters

ValueCountFrequency (%)
04896907
21.7%
14177306
18.6%
23180808
14.1%
-1957898
 
8.7%
:1957898
 
8.7%
31457717
 
6.5%
T978949
 
4.3%
.978949
 
4.3%
4603612
 
2.7%
5602830
 
2.7%
Other values (4)1722953
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16642133
73.9%
Other Punctuation2936847
 
13.0%
Dash Punctuation1957898
 
8.7%
Uppercase Letter978949
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04896907
29.4%
14177306
25.1%
23180808
19.1%
31457717
 
8.8%
4603612
 
3.6%
5602830
 
3.6%
9515729
 
3.1%
7459379
 
2.8%
8448661
 
2.7%
6299184
 
1.8%
Other Punctuation
ValueCountFrequency (%)
:1957898
66.7%
.978949
33.3%
Dash Punctuation
ValueCountFrequency (%)
-1957898
100.0%
Uppercase Letter
ValueCountFrequency (%)
T978949
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21536878
95.7%
Latin978949
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
04896907
22.7%
14177306
19.4%
23180808
14.8%
-1957898
 
9.1%
:1957898
 
9.1%
31457717
 
6.8%
.978949
 
4.5%
4603612
 
2.8%
5602830
 
2.8%
9515729
 
2.4%
Other values (3)1207224
 
5.6%
Latin
ValueCountFrequency (%)
T978949
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22515827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04896907
21.7%
14177306
18.6%
23180808
14.1%
-1957898
 
8.7%
:1957898
 
8.7%
31457717
 
6.5%
T978949
 
4.3%
.978949
 
4.3%
4603612
 
2.7%
5602830
 
2.7%
Other values (4)1722953
 
7.7%

jerseyNumber
Real number (ℝ≥0)

MISSING

Distinct98
Distinct (%)< 0.1%
Missing42563
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean50.15307149
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:55.258506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q123
median52
Q376
95-th percentile96
Maximum99
Range98
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.85567387
Coefficient of variation (CV)0.5952910357
Kurtosis-1.34391869
Mean50.15307149
Median Absolute Deviation (MAD)27
Skewness0.02970864493
Sum46962634
Variance891.3612624
MonotonicityNot monotonic
2022-11-02T11:59:55.390650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2219451
 
2.0%
2418736
 
1.9%
2118678
 
1.9%
1118422
 
1.9%
9717746
 
1.8%
2316895
 
1.7%
7216347
 
1.7%
7116153
 
1.7%
2015904
 
1.6%
2615626
 
1.6%
Other values (88)762428
77.9%
(Missing)42563
 
4.3%
ValueCountFrequency (%)
114516
1.5%
213607
1.4%
32422
 
0.2%
47823
0.8%
55529
 
0.6%
66044
 
0.6%
77516
0.8%
88104
0.8%
96009
 
0.6%
1015113
1.5%
ValueCountFrequency (%)
9911078
1.1%
9812730
1.3%
9717746
1.8%
968984
0.9%
9510028
1.0%
9414303
1.5%
9310202
1.0%
927397
0.8%
9114280
1.5%
9014794
1.5%

team
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
football
 
42563
TEN
 
39391
IND
 
39391
DAL
 
37664
MIN
 
37664
Other values (26)
782276 

Length

Max length8
Median length3
Mean length2.99261555
Min length2

Characters and Unicode

Total characters2929618
Distinct characters29
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARI
2nd rowARI
3rd rowARI
4th rowARI
5th rowARI

Common Values

ValueCountFrequency (%)
football42563
 
4.3%
TEN39391
 
4.0%
IND39391
 
4.0%
DAL37664
 
3.8%
MIN37664
 
3.8%
KC36498
 
3.7%
NYG36498
 
3.7%
SEA34529
 
3.5%
JAX34529
 
3.5%
LAC34441
 
3.5%
Other values (21)605781
61.9%

Length

2022-11-02T11:59:55.511812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
football42563
 
4.3%
ind39391
 
4.0%
ten39391
 
4.0%
dal37664
 
3.8%
min37664
 
3.8%
kc36498
 
3.7%
nyg36498
 
3.7%
sea34529
 
3.5%
jax34529
 
3.5%
lac34441
 
3.5%
Other values (21)605781
61.9%

Most occurring characters

ValueCountFrequency (%)
N317108
 
10.8%
A303226
 
10.4%
I246114
 
8.4%
E190663
 
6.5%
C182336
 
6.2%
L151712
 
5.2%
T150260
 
5.1%
D133100
 
4.5%
S91718
 
3.1%
B89012
 
3.0%
Other values (19)1074369
36.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2589114
88.4%
Lowercase Letter340504
 
11.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N317108
12.2%
A303226
11.7%
I246114
 
9.5%
E190663
 
7.4%
C182336
 
7.0%
L151712
 
5.9%
T150260
 
5.8%
D133100
 
5.1%
S91718
 
3.5%
B89012
 
3.4%
Other values (13)733865
28.3%
Lowercase Letter
ValueCountFrequency (%)
l85126
25.0%
o85126
25.0%
f42563
12.5%
a42563
12.5%
b42563
12.5%
t42563
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2929618
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N317108
 
10.8%
A303226
 
10.4%
I246114
 
8.4%
E190663
 
6.5%
C182336
 
6.2%
L151712
 
5.2%
T150260
 
5.1%
D133100
 
4.5%
S91718
 
3.1%
B89012
 
3.0%
Other values (19)1074369
36.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2929618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N317108
 
10.8%
A303226
 
10.4%
I246114
 
8.4%
E190663
 
6.5%
C182336
 
6.2%
L151712
 
5.2%
T150260
 
5.1%
D133100
 
4.5%
S91718
 
3.1%
B89012
 
3.0%
Other values (19)1074369
36.7%

playDirection
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
left
517454 
right
461495 

Length

Max length5
Median length4
Mean length4.471418838
Min length4

Characters and Unicode

Total characters4377291
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowright
3rd rowright
4th rowright
5th rowright

Common Values

ValueCountFrequency (%)
left517454
52.9%
right461495
47.1%

Length

2022-11-02T11:59:55.607085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T11:59:55.698603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
left517454
52.9%
right461495
47.1%

Most occurring characters

ValueCountFrequency (%)
t978949
22.4%
l517454
11.8%
e517454
11.8%
f517454
11.8%
r461495
10.5%
i461495
10.5%
g461495
10.5%
h461495
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4377291
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t978949
22.4%
l517454
11.8%
e517454
11.8%
f517454
11.8%
r461495
10.5%
i461495
10.5%
g461495
10.5%
h461495
10.5%

Most occurring scripts

ValueCountFrequency (%)
Latin4377291
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t978949
22.4%
l517454
11.8%
e517454
11.8%
f517454
11.8%
r461495
10.5%
i461495
10.5%
g461495
10.5%
h461495
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4377291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t978949
22.4%
l517454
11.8%
e517454
11.8%
f517454
11.8%
r461495
10.5%
i461495
10.5%
g461495
10.5%
h461495
10.5%

x
Real number (ℝ≥0)

Distinct11695
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.58592922
Minimum0.47
Maximum119.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:55.790155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.47
5-th percentile17.88
Q138.81
median58.84
Q377.78
95-th percentile99.6
Maximum119.98
Range119.51
Interquartile range (IQR)38.97

Descriptive statistics

Standard deviation24.92508577
Coefficient of variation (CV)0.4254449165
Kurtosis-0.8152433424
Mean58.58592922
Median Absolute Deviation (MAD)19.48
Skewness0.0216178471
Sum57352636.82
Variance621.2599004
MonotonicityNot monotonic
2022-11-02T11:59:56.079648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.3179
 
< 0.1%
66.34176
 
< 0.1%
75.05172
 
< 0.1%
68.67171
 
< 0.1%
63.53171
 
< 0.1%
69.19171
 
< 0.1%
72.14171
 
< 0.1%
68.69171
 
< 0.1%
74.11170
 
< 0.1%
33.66170
 
< 0.1%
Other values (11685)977227
99.8%
ValueCountFrequency (%)
0.471
< 0.1%
0.721
< 0.1%
0.781
< 0.1%
0.861
< 0.1%
0.951
< 0.1%
0.991
< 0.1%
1.061
< 0.1%
1.181
< 0.1%
1.251
< 0.1%
1.281
< 0.1%
ValueCountFrequency (%)
119.981
< 0.1%
119.972
< 0.1%
119.951
< 0.1%
119.941
< 0.1%
119.911
< 0.1%
119.891
< 0.1%
119.811
< 0.1%
119.691
< 0.1%
119.551
< 0.1%
119.511
< 0.1%

y
Real number (ℝ)

Distinct5333
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.71448044
Minimum-4.53
Maximum53.63
Zeros0
Zeros (%)0.0%
Negative65
Negative (%)< 0.1%
Memory size7.5 MiB
2022-11-02T11:59:56.209819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.53
5-th percentile11.66
Q121.88
median26.68
Q331.49
95-th percentile42.14
Maximum53.63
Range58.16
Interquartile range (IQR)9.61

Descriptive statistics

Standard deviation8.305238378
Coefficient of variation (CV)0.3108890101
Kurtosis0.3033364944
Mean26.71448044
Median Absolute Deviation (MAD)4.81
Skewness0.02051996881
Sum26152113.91
Variance68.97698452
MonotonicityNot monotonic
2022-11-02T11:59:56.329879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.811167
 
0.1%
23.781099
 
0.1%
23.871044
 
0.1%
29.761023
 
0.1%
23.831018
 
0.1%
23.861014
 
0.1%
23.751000
 
0.1%
23.79990
 
0.1%
23.76986
 
0.1%
23.77982
 
0.1%
Other values (5323)968626
98.9%
ValueCountFrequency (%)
-4.531
< 0.1%
-4.211
< 0.1%
-3.911
< 0.1%
-3.561
< 0.1%
-3.261
< 0.1%
-3.181
< 0.1%
-2.981
< 0.1%
-2.861
< 0.1%
-2.631
< 0.1%
-2.611
< 0.1%
ValueCountFrequency (%)
53.631
 
< 0.1%
53.351
 
< 0.1%
53.331
 
< 0.1%
53.251
 
< 0.1%
53.232
< 0.1%
53.221
 
< 0.1%
53.211
 
< 0.1%
53.191
 
< 0.1%
53.182
< 0.1%
53.173
< 0.1%

s
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2173
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.588453495
Minimum0
Maximum28.16
Zeros60657
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:56.460073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.76
median2.15
Q33.82
95-th percentile6.76
Maximum28.16
Range28.16
Interquartile range (IQR)3.06

Descriptive statistics

Standard deviation2.393862993
Coefficient of variation (CV)0.9248236438
Kurtosis14.50736989
Mean2.588453495
Median Absolute Deviation (MAD)1.5
Skewness2.372197137
Sum2533963.96
Variance5.730580028
MonotonicityNot monotonic
2022-11-02T11:59:56.580558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060657
 
6.2%
0.0115215
 
1.6%
0.028942
 
0.9%
0.036575
 
0.7%
0.045304
 
0.5%
0.054702
 
0.5%
0.064191
 
0.4%
0.074002
 
0.4%
0.083736
 
0.4%
0.093478
 
0.4%
Other values (2163)862147
88.1%
ValueCountFrequency (%)
060657
6.2%
0.0115215
 
1.6%
0.028942
 
0.9%
0.036575
 
0.7%
0.045304
 
0.5%
0.054702
 
0.5%
0.064191
 
0.4%
0.074002
 
0.4%
0.083736
 
0.4%
0.093478
 
0.4%
ValueCountFrequency (%)
28.161
< 0.1%
28.021
< 0.1%
27.711
< 0.1%
27.551
< 0.1%
27.341
< 0.1%
27.31
< 0.1%
27.261
< 0.1%
27.211
< 0.1%
27.011
< 0.1%
271
< 0.1%

a
Real number (ℝ≥0)

ZEROS

Distinct1518
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.80429337
Minimum0
Maximum33.43
Zeros56618
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:56.716033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.72
median1.55
Q32.6
95-th percentile4.49
Maximum33.43
Range33.43
Interquartile range (IQR)1.88

Descriptive statistics

Standard deviation1.442816763
Coefficient of variation (CV)0.7996575206
Kurtosis6.401140116
Mean1.80429337
Median Absolute Deviation (MAD)0.92
Skewness1.409875764
Sum1766311.19
Variance2.08172021
MonotonicityNot monotonic
2022-11-02T11:59:56.839337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
056618
 
5.8%
0.0111996
 
1.2%
0.026956
 
0.7%
0.035202
 
0.5%
0.043956
 
0.4%
0.053360
 
0.3%
1.343126
 
0.3%
1.283114
 
0.3%
1.053089
 
0.3%
1.063073
 
0.3%
Other values (1508)878459
89.7%
ValueCountFrequency (%)
056618
5.8%
0.0111996
 
1.2%
0.026956
 
0.7%
0.035202
 
0.5%
0.043956
 
0.4%
0.053360
 
0.3%
0.062989
 
0.3%
0.072759
 
0.3%
0.082330
 
0.2%
0.092306
 
0.2%
ValueCountFrequency (%)
33.431
< 0.1%
32.571
< 0.1%
30.981
< 0.1%
28.721
< 0.1%
26.581
< 0.1%
26.281
< 0.1%
25.611
< 0.1%
24.161
< 0.1%
23.781
< 0.1%
23.621
< 0.1%

dis
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct535
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.261897249
Minimum0
Maximum10.45
Zeros62790
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:56.971912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.22
Q30.38
95-th percentile0.68
Maximum10.45
Range10.45
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2563842877
Coefficient of variation (CV)0.9789499077
Kurtosis51.57842879
Mean0.261897249
Median Absolute Deviation (MAD)0.15
Skewness4.235698019
Sum256384.05
Variance0.06573290299
MonotonicityNot monotonic
2022-11-02T11:59:57.096162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062790
 
6.4%
0.0153725
 
5.5%
0.0229770
 
3.0%
0.0322761
 
2.3%
0.0420019
 
2.0%
0.0518459
 
1.9%
0.218137
 
1.9%
0.2117919
 
1.8%
0.1717857
 
1.8%
0.1917820
 
1.8%
Other values (525)699692
71.5%
ValueCountFrequency (%)
062790
6.4%
0.0153725
5.5%
0.0229770
3.0%
0.0322761
 
2.3%
0.0420019
 
2.0%
0.0518459
 
1.9%
0.0617699
 
1.8%
0.0717235
 
1.8%
0.0817096
 
1.7%
0.0917069
 
1.7%
ValueCountFrequency (%)
10.451
< 0.1%
8.531
< 0.1%
6.672
< 0.1%
6.411
< 0.1%
6.231
< 0.1%
6.131
< 0.1%
6.121
< 0.1%
6.11
< 0.1%
6.082
< 0.1%
6.021
< 0.1%

o
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.8%
Missing42563
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean180.1493396
Minimum0
Maximum360
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:57.221265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.69
Q190
median179.68
Q3269.29
95-th percentile328.67
Maximum360
Range360
Interquartile range (IQR)179.29

Descriptive statistics

Standard deviation98.62727364
Coefficient of variation (CV)0.5474750773
Kurtosis-1.368621125
Mean180.1493396
Median Absolute Deviation (MAD)89.68
Skewness0.002297522931
Sum168689319.5
Variance9727.339106
MonotonicityNot monotonic
2022-11-02T11:59:57.343289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
901570
 
0.2%
266.33104
 
< 0.1%
90.5995
 
< 0.1%
86.0595
 
< 0.1%
91.3494
 
< 0.1%
90.9394
 
< 0.1%
88.8192
 
< 0.1%
93.9392
 
< 0.1%
271.6692
 
< 0.1%
268.5292
 
< 0.1%
Other values (35991)933966
95.4%
(Missing)42563
 
4.3%
ValueCountFrequency (%)
09
< 0.1%
0.0111
< 0.1%
0.0222
< 0.1%
0.0311
< 0.1%
0.0413
< 0.1%
0.0516
< 0.1%
0.0613
< 0.1%
0.0722
< 0.1%
0.0817
< 0.1%
0.0917
< 0.1%
ValueCountFrequency (%)
36010
< 0.1%
359.999
< 0.1%
359.9818
< 0.1%
359.9713
< 0.1%
359.9615
< 0.1%
359.9513
< 0.1%
359.9413
< 0.1%
359.9314
< 0.1%
359.928
< 0.1%
359.9113
< 0.1%

dir
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.8%
Missing42563
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean180.7110978
Minimum0
Maximum360
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2022-11-02T11:59:57.474782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.79
Q191.61
median180.29
Q3270.36
95-th percentile336.32
Maximum360
Range360
Interquartile range (IQR)178.75

Descriptive statistics

Standard deviation100.5814224
Coefficient of variation (CV)0.5565868594
Kurtosis-1.2830718
Mean180.7110978
Median Absolute Deviation (MAD)89.35
Skewness-0.0003477824361
Sum169215342
Variance10116.62253
MonotonicityNot monotonic
2022-11-02T11:59:57.600026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266.670
 
< 0.1%
89.9370
 
< 0.1%
261.169
 
< 0.1%
88.6568
 
< 0.1%
88.1367
 
< 0.1%
263.0167
 
< 0.1%
267.7767
 
< 0.1%
87.3966
 
< 0.1%
96.6666
 
< 0.1%
272.4166
 
< 0.1%
Other values (35991)935710
95.6%
(Missing)42563
 
4.3%
ValueCountFrequency (%)
08
 
< 0.1%
0.0125
< 0.1%
0.0220
< 0.1%
0.0311
< 0.1%
0.0418
< 0.1%
0.0516
< 0.1%
0.0615
< 0.1%
0.0720
< 0.1%
0.0819
< 0.1%
0.0924
< 0.1%
ValueCountFrequency (%)
3606
 
< 0.1%
359.9919
< 0.1%
359.9823
< 0.1%
359.9723
< 0.1%
359.9622
< 0.1%
359.9528
< 0.1%
359.9415
< 0.1%
359.9316
< 0.1%
359.9219
< 0.1%
359.9122
< 0.1%

event
Categorical

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
None
901968 
ball_snap
 
23713
pass_forward
 
21137
autoevent_ballsnap
 
10534
autoevent_passforward
 
10143
Other values (18)
 
11454

Length

Max length25
Median length4
Mean length4.697930127
Min length3

Characters and Unicode

Total characters4599034
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None901968
92.1%
ball_snap23713
 
2.4%
pass_forward21137
 
2.2%
autoevent_ballsnap10534
 
1.1%
autoevent_passforward10143
 
1.0%
play_action5382
 
0.5%
qb_sack1265
 
0.1%
run1058
 
0.1%
pass_arrived989
 
0.1%
autoevent_passinterrupted483
 
< 0.1%
Other values (13)2277
 
0.2%

Length

2022-11-02T11:59:57.724766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none901968
92.1%
ball_snap23713
 
2.4%
pass_forward21137
 
2.2%
autoevent_ballsnap10534
 
1.1%
autoevent_passforward10143
 
1.0%
play_action5382
 
0.5%
qb_sack1265
 
0.1%
run1058
 
0.1%
pass_arrived989
 
0.1%
autoevent_passinterrupted483
 
< 0.1%
Other values (13)2277
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n965862
21.0%
o961262
20.9%
e948727
20.6%
N901968
19.6%
a168176
 
3.7%
s103822
 
2.3%
_76222
 
1.7%
p74796
 
1.6%
l74681
 
1.6%
r67183
 
1.5%
Other values (15)256335
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3620844
78.7%
Uppercase Letter901968
 
19.6%
Connector Punctuation76222
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n965862
26.7%
o961262
26.5%
e948727
26.2%
a168176
 
4.6%
s103822
 
2.9%
p74796
 
2.1%
l74681
 
2.1%
r67183
 
1.9%
t51451
 
1.4%
b35880
 
1.0%
Other values (13)169004
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
N901968
100.0%
Connector Punctuation
ValueCountFrequency (%)
_76222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4522812
98.3%
Common76222
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n965862
21.4%
o961262
21.3%
e948727
21.0%
N901968
19.9%
a168176
 
3.7%
s103822
 
2.3%
p74796
 
1.7%
l74681
 
1.7%
r67183
 
1.5%
t51451
 
1.1%
Other values (14)204884
 
4.5%
Common
ValueCountFrequency (%)
_76222
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4599034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n965862
21.0%
o961262
20.9%
e948727
20.6%
N901968
19.6%
a168176
 
3.7%
s103822
 
2.3%
_76222
 
1.7%
p74796
 
1.6%
l74681
 
1.6%
r67183
 
1.5%
Other values (15)256335
 
5.6%

Interactions

2022-11-02T11:59:46.455923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:11.693387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:14.881808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:17.908707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:21.019901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:24.223387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:27.325552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:30.447720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:33.678006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:36.847645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:40.004307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:43.257127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:46.746450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:11.954888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:15.134071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:18.168755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:21.281193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:24.483543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:27.592275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:30.710564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:33.948061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:37.120152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:40.421087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:43.528585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:47.038545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:12.206268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:15.380277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:18.415901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:21.533089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:24.732636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:27.841157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:30.958759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:34.208666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:37.380732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:40.675750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:43.787456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:47.325916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:12.459102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:15.630330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:18.674860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:21.781654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:24.992633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:28.100961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:31.211306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:34.470894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:37.642195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:40.936619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:44.054279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:47.588595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:12.713441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:15.883571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:18.931509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:22.032211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:25.241148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:28.355310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:31.461812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:34.732145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:37.905418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:41.194232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:44.320755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:47.850652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:12.970067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:16.141840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:19.192104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:22.284042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:25.495132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:28.616977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:31.867534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:34.990559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:38.168130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:41.448008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:44.580890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:48.117544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:13.225713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:16.388796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:19.451832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:22.538022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:25.749488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:28.882828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:32.123160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:35.264537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:38.436684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:41.698668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:44.846746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:48.380114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:13.478012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:16.640749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:19.709102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:22.795235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:26.002261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:29.143986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:32.374876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:35.516171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:38.695074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:41.948384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:45.106665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:48.642589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:13.728845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:16.896432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:19.970517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:23.044751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:26.254294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:29.404321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:32.626221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:35.778881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:38.945358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:42.198523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:45.367420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:49.059027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:13.984227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:17.151303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:20.228188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:23.456814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:26.505085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:29.666633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:32.894477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:36.045358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:39.218647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:42.451029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:45.624617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:49.330611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:14.238155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:17.399121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:20.498234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:23.710920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:26.783064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:29.919470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:33.150224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:36.305879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:39.479182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:42.711128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:45.909923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:49.593587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:14.499302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:17.654155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:20.762322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:23.969475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:27.060600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:30.181319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:33.416355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:36.577298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:39.750909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:42.987582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T11:59:46.181547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T11:59:57.822683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-02T11:59:57.974216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T11:59:58.119292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T11:59:58.264704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T11:59:58.560258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-02T11:59:58.669885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T11:59:50.132930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T11:59:51.280206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-02T11:59:52.931398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-02T11:59:53.560340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
0202110280018937077.012021-10-29T00:27:23.00018.0ARIright21.386.940.000.000.0043.74223.19None
1202110280018937077.022021-10-29T00:27:23.10018.0ARIright21.386.940.000.000.0044.67243.81None
2202110280018937077.032021-10-29T00:27:23.20018.0ARIright21.386.950.000.000.0045.69303.24None
3202110280018937077.042021-10-29T00:27:23.30018.0ARIright21.386.940.000.000.0046.44285.89None
4202110280018937077.052021-10-29T00:27:23.40018.0ARIright21.386.950.000.000.0147.99341.60None
5202110280018937077.062021-10-29T00:27:23.50018.0ARIright21.396.940.010.200.0050.8580.97ball_snap
6202110280018937077.072021-10-29T00:27:23.60018.0ARIright21.406.950.080.700.0152.4977.83None
7202110280018937077.082021-10-29T00:27:23.70018.0ARIright21.446.950.442.460.0451.8282.23None
8202110280018937077.092021-10-29T00:27:23.80018.0ARIright21.516.960.803.010.0753.3782.37None
9202110280018937077.0102021-10-29T00:27:23.90018.0ARIright21.626.981.223.310.1156.2681.58None

Last rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
97893920211101004433NaN492021-11-02T03:20:26.000NaNfootballright23.3926.842.730.880.28NaNNaNNone
97894020211101004433NaN502021-11-02T03:20:26.100NaNfootballright23.5027.102.810.820.28NaNNaNNone
97894120211101004433NaN512021-11-02T03:20:26.200NaNfootballright23.6427.332.721.590.27NaNNaNNone
97894220211101004433NaN522021-11-02T03:20:26.300NaNfootballright23.8027.542.632.150.26NaNNaNNone
97894320211101004433NaN532021-11-02T03:20:26.400NaNfootballright23.9927.722.572.270.26NaNNaNqb_strip_sack
97894420211101004433NaN542021-11-02T03:20:26.500NaNfootballright24.1727.892.472.280.25NaNNaNNone
97894520211101004433NaN552021-11-02T03:20:26.600NaNfootballright24.3628.032.362.160.24NaNNaNNone
97894620211101004433NaN562021-11-02T03:20:26.700NaNfootballright24.5528.172.251.450.23NaNNaNNone
97894720211101004433NaN572021-11-02T03:20:26.800NaNfootballright24.7328.312.280.720.23NaNNaNNone
97894820211101004433NaN582021-11-02T03:20:26.900NaNfootballright24.9128.452.320.540.23NaNNaNNone